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Data Proc
Lab: Creating And Managing A Dataproc Cluster
Lab: Creating A Firewall Rule To Access Dataproc
Lab: Running A PySpark Job On Dataproc
Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc
Lab: Submitting A Spark Jar To Dataproc
Lab: Working With Dataproc Using The GCloud CLI

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Pub Sub
Lab: Working With Pubsub On The Command Line
Lab: Working With PubSub Using The Web Console
Lab: Setting Up A Pubsub Publisher Using The Python Library
Lab: Setting Up A Pubsub Subscriber Using The Python Library
Lab: Publishing Streaming Data Into Pubsub
Lab: Reading Streaming Data From PubSub And Writing To BigQuery
Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery
Lab: Pubsub Source BigQuery Sink

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Data Lab
Lab: Creating And Working On A Datalab Instance
Lab: Importing And Exporting Data Using Datalab
Lab: Using The Charting API In Datalab

Course Description

Use big data technologies such as BigTable, Dataflow, Apache Beam and Pub/Sub

About the course

This course is a really comprehensive guide to the Google Cloud Platform - it has ~25 hours of content and ~60 demos.

The Google Cloud Platform is not currently the most popular cloud offering out there - that's AWS of course - but it is possibly the best cloud offering for high-end machine learning applications. That's because TensorFlow, the super-popular deep learning technology is also from Google.